> For the complete documentation index, see [llms.txt](https://docs.flashback.tech/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://docs.flashback.tech/guides/explore-use-cases/ai-llm.md).

# AI LLM

This page introduces practical AI application patterns built on Flashgate’s OpenAI-compatible AI Gateway.

The goal is to help teams deploy production-grade LLM workflows with:

* centralized credential management,
* repository-level API keys,
* policy enforcement and observability,
* model/provider portability.

## Available use cases

* [**Multi-model fallback and reliability routing**](/guides/explore-use-cases/ai-llm/multi-model-fallback-and-reliability-routing.md)
* [**Cost guardrails with automatic model tiering**](/guides/explore-use-cases/ai-llm/cost-guardrails-with-automatic-model-tiering.md)
* [**PII-safe support assistant with policy enforcement**](/guides/explore-use-cases/ai-llm/pii-safe-support-assistant-with-policy-enforcement.md)
* [**RAG knowledge assistant over multi-cloud storage**](/guides/explore-use-cases/ai-llm/rag-knowledge-assistant-over-multi-cloud-storage.md)

## Before you start

Make sure you have:

1. At least one configured AI provider in Flashgate (AI → AI LLM).
2. A repository exposing an OpenAI-compatible endpoint.
3. Repository API keys for your application.
4. Optional governance rules in AI Policy for production workloads.


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